A Deep Web Complex Matching Method Based on Association Mining and Semantic Clustering
Yang Xiaoqin, Shiguang Ju, Cao Qinghuang · 2009
In order to improve the efficiency and accuracy of Deep Web interface matching, a method based on the existing Dual Correlation Mining (DCM) method using association mining and semantic clustering was presented in this paper. While digging group attributes by using correlation algorithm, a new correlation measure based on Mutual Information was introduced and realized by matrix to resolve the inefficiency problem. The attributes were clustered to synonymous attributes by their similarity which was computed by using semantic net. By the compare on more than 200 interfaces in 4 domains, the experiment results indicate that the improved method has greatly heighted than DCM in the respect of efficiency and accuracy.